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Behavioral data Categorization for Transformers-based models in Digital Health

Behavioral Data Categorization for Transformers-based Models...

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4th IEEE-EMBS international conference on Wearable and Implantable Body Sensor Networks (BSN) / 18th IEEE-EMBS international conference on Biomedical and Health Informatics (BHI)

作者： Siebra, Clauirton Matias, Igor Wac, Katarzyna Univ Geneva Qual Life Technol Lab Geneva Switzerland

ISBN: (数字)9781665487917

ISBN: (纸本)9781665487917

Transformers are recent deep learning (DL) models used to capture the dependence between parts of sequential data. While their potential was already demonstrated in the natural language processing (NLP) domain, emerging research shows transformers can also be an adequate modeling approach to relate longitudinal multi-featured continuous behavioral data to future health outcomes. As transformers-based predictions are based on a domain lexicon, the use of categories, commonly used in specialized areas to cluster values, is the likely way to compose lexica. However, the number of categories may influence the transformer prediction accuracy, mainly when the categorization process creates imbalanced datasets, or the search space is very restricted to generate optimal feasible solutions. This paper analyzes the relationship between models' accuracy and the sparsity of behavioral data categories that compose the lexicon. This analysis relies on a case example that uses mQoL-Transformer to model the influence of physical activity behavior on sleep health. Results show that the number of categories shall be treated as a further transformer's hyperparameter, which can balance the literature-based categorization and optimization aspects. Thus, DL processes could also obtain similar accuracies compared to traditional approaches, such as long short-term memory, when used to process short behavioral data sequences.

关键词： deep learning transformers human behavior recommendations behavior informatics digital biomarkers

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Distributed Energy Resources Hosting Capacity Assessment - An Industry Perspective

Distributed Energy Resources Hosting Capacity Assessment - A...

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2023 IEEE international conference on Energy Technologies for Future Grids, ETFG 2023

作者： Makhalfih, Asaad Hossain, M.J. MacAna, Carlos Pota, Hemanshu Roy School of Electrical and Data Engineering Sydney Australia Essential Energy Network Modelling Services Canberra Australia School of Engineering and Information Technology Canberra Australia

ISBN: (纸本)9781665471640

Hosting Capacity (HC) analysis is a recent methodology that is used by many Low Voltage Distribution Network (LVDN) operators to address the technical limits associated with Distributed Energy Resources (DER) integration into the grid. In the literature, several techniques and methodologies have been proposed to perform the HC analysis. However, there is a gap between existing approaches in the literature and those which are utilised in the industry to assess HC. In this paper, the HC techniques used by the industry have been reviewed. The main industrial HC techniques are, namely, iterative HC, stochastic HC and streamlined HC. Unlike other papers, the methods have been presented in a more technical way to help other researchers and engineers reproduce the algorithms. Moreover, the three methods have been applied to the European low-voltage test feeder to compare their performance against accuracy, processing efficiency and complexity. It was found that the iterative method has the highest accuracy while the Streamlined approach possesses the lowest processing time. © 2023 IEEE.

关键词： Distributed energy resources Hosting capacity Low voltage distribution networks PV

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Identification of batch reactor process using Extreme Learning Machine based Hammerstein and Hammerstein-Wiener models

Identification of batch reactor process using Extreme Learni...

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2024 international conference on Thermofluids and Manufacturing Science, ICTMS 2024

作者： Balakrishnan, Murugan Rajendran, Vinodha Prajwal, Shettigar J. Indiran, Thirunavukkarasu Department of Electronics and Instrumentation Engineering Annamalai University Annamalai Nagar Tamilnadu India Department of Instrumentation and Control Engineering Institution Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

This paper concentrates on system identification using neural networks, specifically avoiding the need for gradient calculation. In our proposed approach, the parameters of the nonlinear static blocks in both Hammerstein and Hammerstein-Wiener models are represented as Single Hidden Layer Feed forward Networks (SLFNs). The identification of both nonlinear and linear parameters is accomplished through the application of Extreme Learning Machine (ELM). In ELM, the hidden layer weights and biases are generated randomly and remain fixed, whereas the output weights are computed using linear regression. The training process involves a forward pass, in which the hidden layer activations are computed, followed by the computation of the output weights and backward pass is exempted, where iterative optimization based weight updates are done. To show the efficacy of the ELM based block oriented models, intense nonlinear batch reactor process is identified with its input output data. One can take away the best model fit for controller design from this ELM based modelling. © Published under licence by IOP Publishing Ltd.

关键词： Batch data processing

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A Machine Learning-Based Fall Risk Prediction Model for Chinese Older Adults

A Machine Learning-Based Fall Risk Prediction Model for Chin...

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Image processing, Computer Vision and Machine Learning (ICICML), international conference on

作者： Jie Ren Shumin Yin Fengjun Li Fang Xia Cheng Ding School of Management Changchun University of Chinese Medicine Changchun China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

OBJECTIVE: To predict the risk of falls in older Chinese adults, and to develop a machine learning model for assessing fall risk based on longitudinal study data, which can help identify high-risk individuals and provide a basis for formulating effective interventions. METHODS: A total of 2350 elderly people who participated in CHARLS during 2020 were selected for the study, and the prediction model was constructed. Several machine learning algorithms (logistic regression, K-nearest neighbor, random forest, neural network, decision tree) were used to assess fall risk. Explore the best cut-off points and adjust parameters in the training set, compare the prediction accuracy of the models in the test set, and further explain the best-performing models. RESULTS:Out of the 26 features analyzed, LASSO regression identified 12 features that were critical to model building. Comparing the performance of five different machine learning models on the test set, the results showed that the RF model performed best on various evaluation measures, with a threshold of 0.168, a specificity of 0.860, a sensitivity of 0.526, an accuracy of 0.693, an F1 score of 0.653 and an area under the ROC curve (AUC) of 0.738. .CONCLUSION:: The predictive model of machine learning can effectively evaluate the risk of falls in healthy elderly people. Random Forest (RF) methods clearly explain personalized fall risk prediction.

关键词： Radio frequency Training Accuracy Machine learning algorithms Sensitivity Computational modeling Predictive models data models Older adults Random forests

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Securing Critical Infrastructure: A Denoising data-Driven Approach for Intrusion Detection in ICS Network 6

Securing Critical Infrastructure: A Denoising Data-Driven Ap...

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6th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Izuazu, Urslla Uchechi Ihekoronye, Vivian Ukamaka Kim, Dong-Seong Lee, Jae Min Kumoh National Institute of Technology Department of IT Convergence Engineering Gumi Korea Republic of

ISBN: (纸本)9798350344349

The centralized and vulnerable nature of the industrial control system (ICS) communication network makes it an attractive target for malicious actors aiming to infiltrate and exploit vulnerabilities. These threat actors seek to cause disruptions, compromise sensitive data, and potentially sabotage critical industrial processes. Existing methods for threat detection assume an ideal scenario where there exists no noise/disturbance to threat detection and classification, neglecting to account for the inherent noise and complexity present in real-world industrial processing environments. In reality, the deployment of these models may introduce performance degradation leading to sub-optimal model performance. In response to the identified issue, this study presents a security framework that proactively addresses the challenges posed by noise and provides a robust mechanism for detecting malicious activities from routine industrial network operations. The proposed framework can be deployed at the supervision network segment of ICS to analyze incoming network traffic signals, to effectively distinguish an attack from normal operation amdist noise. Our proposed approach undergoes experimental simulations to validate its effectiveness, and is compared with state-of-The-Art based on key performance metrics. Simulation results show that our approach is robust in reconstructing noisy traffic signals with a minimal mean square error of 0.12 and an overall accuracy of 99.6%, outperforming existing methods. © 2024 IEEE.

关键词： Intrusion detection

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ODIM: Outlier Detection via Likelihood of Under-Fitted Generative models 41

ODIM: Outlier Detection via Likelihood of Under-Fitted Gener...

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41st international conference on Machine Learning, ICML 2024

作者： Kim, Dongha Hwang, Jaesung Lee, Jongjin Kim, Kunwoong Kim, Yongdai Department of Statistics and Data Science Center Sungshin Women's University Seoul Korea Republic of SK Telecom Seoul Korea Republic of Samsung Research Seoul Korea Republic of Department of Statistics Seoul National University Seoul Korea Republic of

The unsupervised outlier detection (UOD) problem refers to a task to identify inliers given training data which contain outliers as well as inliers, without any labeled information about inliers and outliers. It has been widely recognized that using fully-trained likelihood-based deep generative models (DGMs) often results in poor performance in distinguishing inliers from outliers. In this study, we claim that the likelihood itself could serve as powerful evidence for identifying inliers in UOD tasks, provided that DGMs are carefully under-fitted. Our approach begins with a novel observation called the inlier-memorization (IM) effect-when training a deep generative model with data including outliers, the model initially memorizes inliers before outliers. Based on this finding, we develop a new method called the outlier detection via the IM effect (ODIM). Remarkably, the ODIM requires only a few updates, making it computationally efficient-at least tens of times faster than other deep-learning-based algorithms. Also, the ODIM filters out outliers excellently, regardless of the data type, including tabular, image, and text data. To validate the superiority and efficiency of our method, we provide extensive empirical analyses on close to 60 datasets. Copyright 2024 by the author(s)

关键词： Statistics

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th international conference on Big data Computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Enterprise Resource Dynamic Scheduling Model based on Optimized Connectionist Text Proposal Network

Enterprise Resource Dynamic Scheduling Model based on Optimi...

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Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE international conference on

作者： Xing Lu Mingjun Lan Ling Zhang Ying Wu Na Zhang Fengcheng Oilfield Operation Area PetroChina Xinjiang Oilfield Company Karamay China

ISBN: (纸本)9798350355260

With the increasing frequency of business competition between enterprises, information technology and computer science have become decisive factors in determining whether a company wins. The long-term stable development of a company not only depends on its production and operation status, but also on its intelligent management methods. Therefore, an enterprise resource dynamic scheduling and planning model based on optimized CTPN algorithm is introduced, combined with machine learning of a large amount of enterprise resource data, to achieve accurate definition of enterprise resources and realize dynamic scheduling and planning. The results show that the proposed CTPN algorithm has better processing performance than other image processing algorithms, with an average accuracy increase of 6.34%, 5.23% and 2.87% over SGA, AGA and pre-optimization CTPN algorithms, respectively; the number of paths of the proposed algorithm in the fusion of task data is significantly more than that of the control experimental group, which indicates that it is easier to find the best strategy for the dynamic planning of work resources; and the The proposed algorithm as an experimental group reduces the average computation time by 7.43 s compared with the control group, which indicates that the method can effectively carry out dynamic resource planning and scheduling in enterprise enterprise management, realize enterprise digital management, and help enterprises reduce costs and increase efficiency.

关键词： Machine learning algorithms Accuracy Heuristic algorithms Computational modeling Machine learning Companies Dynamic scheduling data models Planning Resource management

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Prediction models of Arterial Pressure during CPR Based on Force data of Manual Chest Compression 8

Prediction Models of Arterial Pressure during CPR Based on F...

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8th IEEE international conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Sun, Mingze Li, Ke Jiang, Lijun Xu, Fengyang Wang, Jiali Xu, Feng Chen, Yuguo Institute of Intelligent Medical Engineering School of Control Science and Engineering Shandong University Jinan China Shandong University Qilu Hospital Jinan China

ISBN: (纸本)9798350300178

Chest compressions are essential for CPR and the quality of it can be evaluated in terms of physical parameters: force, frequency, or physiological parameters: mean arterial pressure (MAP). In this study, an animal model was finished for CPR experiment. The data of the force of manual chest compression and the arterial blood pressure of animals were collected during the experiment, and the force, frequency, MAP and pulse pressure (PP) of manual chest compression were obtained through data processing. In this study, linear fitting was used to solve the linear relationship between force-MAP, frequency-MAP, force-PP and frequency-PP. The relationship between frequency-force-MAP and the relationship of frequency-force-PP was constructed and predicted by the method of RBF and LSTM. As the results showed that the best linear fitting results are frequency-MAP (R-square = 0.58, RMSE = 9.51) and frequency-PP (R-square = 0.62, RMSE = 9.00), and the best prediction results are the PP prediction using the method of RBF (R-square = 0.80, RMSE = 5.73). The method of LSTM got the best result to predict MAP (R-square = 0.74, RMSE = 5.80). © 2023 IEEE.

关键词： Forecasting

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Sentiment Analysis on English and Greek Twitter data Regarding Vaccinations 14

Sentiment Analysis on English and Greek Twitter Data Regardi...

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14th international conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Dontaki, Chrysoula Koukaras, Paraskevas Tjortjis, Christos School of Science & Technology International Hellenic University The Data Mining and Analytics Research Group Thessaloniki Greece

ISBN: (纸本)9798350318067

COVID-19 is one of the most discussed diseases worldwide. To control its spread, several vaccines have been developed and approved. However, low acceptance and worries about the efficacy of vaccines exist due to insufficient information, and social media influence. This study aims to classify people who are in favor or against vaccination, as well as people's preferences for three main types of available vaccines, Pfizer, Moderna and AstraZeneca. Twitter data were retrieved over seven months. This work proposes the adoption of an on-the-fly approach involving a sentiment analyzer that includes Machine Learning (ML). Moreover, this paper compares sentiment analysis techniques by applying the following Supervised ML algorithms: Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), XGBoost Classifier (XGB) and Support Vector Machines (SVM). The dataset was divided into two periods in 2022: summer, and autumn. The main contribution is the achievement of high accuracy and F1-Score, when combining these models. The best results were obtained when tweets were analyzed using TextBlob and DT. Regarding the English tweets, the top accuracy was 97.99%, while the top F1-Score was 97.98% in the summer period. With Greek tweets, the performance was better in the autumn period reaching 99.92% accuracy and F1-Score. © 2023 IEEE.

关键词： COVID-19

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